32 citations · 58 across the 9 of their papers we have counts for
5 papers · 1 filter
Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Network
Dingyi Zhuang, Qingyi Wang, Yunhan Zheng +4
Transportation mode share analysis is important to various real-world transportation tasks as it helps researchers understand the travel behaviors and choices of passengers. A typi…
Fairness-enhancing deep learning for ride-hailing demand prediction
Yunhan Zheng, Qingyi Wang, Dingyi Zhuang +2
Short-term demand forecasting for on-demand ride-hailing services is one of the fundamental issues in intelligent transportation systems. However, previous travel demand forecastin…
Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?
Qingyi Wang, Shenhao Wang, Yunhan Zheng +4
Classical demand modeling analyzes travel behavior using only low-dimensional numeric data (i.e. sociodemographics and travel attributes) but not high-dimensional urban imagery. Ho…
Uncertainty Quantification of Spatiotemporal Travel Demand with Probabilistic Graph Neural Networks
Qingyi Wang, Shenhao Wang, Dingyi Zhuang +2
Recent studies have significantly improved the prediction accuracy of travel demand using graph neural networks. However, these studies largely ignored uncertainty that inevitably…
Predicting Drivers' Route Trajectories in Last-Mile Delivery Using A Pair-wise Attention-based Pointer Neural Network
Baichuan Mo, Qing Yi Wang, Xiaotong Guo +2
In last-mile delivery, drivers frequently deviate from planned delivery routes because of their tacit knowledge of the road and curbside infrastructure, customer availability, and…